Comparar métodos

	Avaliação de Impacto Contrafactual Dinâmica	Avaliação de Impacto Contrafactual (AIC)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1986–2009	1970s–2000s
Autor original	Robins (1986); Lechner (2009) for sequential treatment settings	Heckman, Imbens, Rubin, and the program evaluation literature
Tipo	Causal inference / program evaluation	Causal inference / program evaluation
Fonte seminal	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.
Outros nomes	dynamic CIE, dynamic treatment evaluation, time-varying counterfactual analysis, longitudinal counterfactual evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Relacionados	6	5
Resumo	Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.